This paper presents an Automatic Speech Recognition (ASR) system, in the Gujarati language, developed for Low Resource Speech Recognition Challenge for Indian Languages in INTER-SPEECH 2018. For front-end, Amplitude Modulation (AM) features are extracted using the standard and data-driven auditory filterbanks. Recurrent Neural Network Language Models (RNNLM) are used for this task. There is a relative improvement of 36.18 % and 40.95 % in perplexity on the test and blind test sets, respectively, compared to 3-gram LM. Time-Delay Neural Network (TDNN) and TDNN-Long Short-Term Memory (LSTM) models are employed for acoustic modeling. The statistical significance of proposed approaches is justified using a bootstrap-based % Probability of Improvement (POI) measure. RNNLM rescoring with 3-gram LM gave an absolute reduction of 0.69-1.29 % in Word Error Rate (WER) for various feature sets. AM features extracted using the gammatone filterbank (AM-GTFB) performed well on the blind test set compared to the FBANK baseline (POI>70 %). The combination of ASR systems further increased the performance with an absolute reduction of 1.89 and 2.24 % in WER for test and blind test sets, respectively (100 % POI).

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This paper presents our work on end-to-end (E2E) system development for multiple array track in the CHiME-5 challenge 2018. In particular, we propose to use E2E Lattice Free Maximum Mutual Information (LF-MMI) for acoustic modeling. For front-end, Mel Frequency Spectral Coefficients (MFSC) and Power Normalized Spectral Coefficients (PNSC) features are used. We employ delay-and-sum beamformer for speech enhancement of training and development data. The Recurrent Neural Network Language Model (RNNLM) rescoring is also explored along with 3-gram language model. Our E2E LF-MMI Time Delay Neural Network (TDNN) system performed better than the E2E system provided in the challenge with an absolute reduction of 10.95 % in WER. The final system combination further reduces the WER to 78.63 %. Hence, our proposed system combination captures complementary information due to various E2E systems trained on full training data, beamformed data and using MFSC and PNSC features, respectively.